book

Exploratory Data Analysis with Python Cookbook

Name: Exploratory Data Analysis with Python Cookbook
Author: Ayodele Oluleye
ISBN: 9781803231105

by Ayodele Oluleye

June 2023

Beginner to intermediate

382 pages

7h 52m

English

Packt Publishing

Read now

Unlock full access

Exploratory Data Analysis with Python Cookbook
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Generating Summary Statistics
Technical requirementsAnalyzing the mean of a datasetGetting readyHow to do it…How it works...There’s more...Checking the median of a datasetGetting readyHow to do it…How it works...There’s more...Identifying the mode of a datasetGetting readyHow to do it…How it works...There’s more...Checking the variance of a datasetGetting readyHow to do it…How it works...There’s more…Identifying the standard deviation of a datasetGetting readyHow to do it…How it works...There’s more...Generating the range of a datasetGetting readyHow to do it…How it works...There’s more...Identifying the percentiles of a datasetGetting readyHow to do it…How it works...There’s more...Checking the quartiles of a datasetGetting readyHow to do it…How it works...There’s more...Analyzing the interquartile range (IQR) of a datasetGetting readyHow to do it…How it works...
Chapter 2: Preparing Data for EDA
Technical requirementsGrouping dataGetting readyHow to do it…How it works...There’s more...See alsoAppending dataGetting readyHow to do it…How it works...There’s more...Concatenating dataGetting readyHow to do it…How it works...There’s more...See alsoMerging dataGetting readyHow to do it…How it works...There’s more...See alsoSorting dataGetting readyHow to do it…How it works...There’s more...Categorizing dataGetting readyHow to do it…How it works...There’s more...Removing duplicate dataGetting readyHow to do it…How it works...There’s more...Dropping data rows and columnsGetting readyHow to do it…How it works...There’s more...Replacing dataGetting readyHow to do it…How it works...There’s more...See alsoChanging a data formatGetting readyHow to do it…How it works...There’s more...See alsoDealing with missing valuesGetting readyHow to do it…How it works...There’s more...See also
Chapter 3: Visualizing Data in Python
Technical requirementsPreparing for visualizationGetting readyHow to do it…How it works...There’s more...Visualizing data in MatplotlibGetting readyHow to do it…How it works...There’s more...See alsoVisualizing data in SeabornGetting readyHow to do it…How it works...There’s more...See alsoVisualizing data in GGPLOTGetting readyHow to do it…How it works...There’s more...See alsoVisualizing data in BokehGetting readyHow to do it…How it works...There's more...See also
Chapter 4: Performing Univariate Analysis in Python
Technical requirementsPerforming univariate analysis using a histogramGetting readyHow to do it…How it works...Performing univariate analysis using a boxplotGetting readyHow to do it…How it works...There’s more...Performing univariate analysis using a violin plotGetting readyHow to do it…How it works...Performing univariate analysis using a summary tableGetting readyHow to do it…How it works...There’s more...Performing univariate analysis using a bar chartGetting readyHow to do it…How it works...Performing univariate analysis using a pie chartGetting readyHow to do it…How it works...
Chapter 5: Performing Bivariate Analysis in Python
Technical requirementsAnalyzing two variables using a scatter plotGetting readyHow to do it…How it works...There’s more...See also...Creating a crosstab/two-way table on bivariate dataGetting readyHow to do it…How it works...Analyzing two variables using a pivot tableGetting readyHow to do it…How it works...There is more...Generating pairplots on two variablesGetting readyHow to do it…How it works...Analyzing two variables using a bar chartGetting readyHow to do it…How it works...There is more...Generating box plots for two variablesGetting readyHow to do it…How it works...Creating histograms on two variablesGetting readyHow to do it…How it works...Analyzing two variables using a correlation analysisGetting readyHow to do it…How it works...
Chapter 6: Performing Multivariate Analysis in Python
Technical requirementsImplementing Cluster Analysis on multiple variables using KmeansGetting readyHow to do it…How it works...There is more...See also...Choosing the optimal number of clusters in KmeansGetting readyHow to do it…How it works...There is more...See also...Profiling Kmeans clustersGetting readyHow to do it…How it works...There’s more...Implementing principal component analysis on multiple variablesGetting readyHow to do it…How it works...There is more...See also...Choosing the number of principal componentsGetting readyHow to do it…How it works...Analyzing principal componentsGetting readyHow to do it…How it works...There’s more...See also...Implementing factor analysis on multiple variablesGetting readyHow to do it…How it works...There is more...Determining the number of factorsGetting readyHow to do it…How it works...Analyzing the factorsGetting readyHow to do it…How it works...
Chapter 7: Analyzing Time Series Data in Python
Technical requirementsUsing line and boxplots to visualize time series dataGetting readyHow to do it…How it works...Spotting patterns in time seriesGetting readyHow to do it…How it works...Performing time series data decompositionGetting readyHow to do it…How it works...Performing smoothing – moving averageGetting readyHow to do it…How it works…See also...Performing smoothing – exponential smoothingGetting readyHow to do it…How it works...See also...Performing stationarity checks on time series dataGetting readyHow to do it…How it works...See also…Differencing time series dataGetting readyHow to do it…How it works...Getting readyHow to do it…How it works...See also...
Chapter 8: Analysing Text Data in Python
Technical requirementsPreparing text dataGetting readyHow to do it…How it works...There’s more…See also…Dealing with stop wordsGetting readyHow to do it…How it works...There’s more…Analyzing part of speechGetting readyHow to do it…How it works...Performing stemming and lemmatizationGetting readyHow to do it…How it works...Analyzing ngramsGetting readyHow to do it…How it works...Creating word cloudsGetting readyHow to do it…How it works...Checking term frequencyGetting readyHow to do it…How it works...There’s more…See alsoChecking sentimentsGetting readyHow to do it…How it works...There’s more…See alsoPerforming Topic ModelingGetting readyHow to do it…How it works...Choosing an optimal number of topicsGetting readyHow to do it…How it works...

Chapter 9: Dealing with Outliers and Missing Values
Technical requirementsIdentifying outliersGetting readyHow to do it…How it works...Spotting univariate outliersGetting readyHow to do it…How it works...Finding bivariate outliersGetting readyHow to do it…How it works...Identifying multivariate outliersGetting readyHow to do it…How it works...See alsoFlooring and capping outliersGetting readyHow to do it…How it works...Removing outliersGetting readyHow to do it…How it works...Replacing outliersGetting readyHow to do it…How it works...Identifying missing valuesGetting readyHow to do it…How it works...Dropping missing valuesGetting readyHow to do it…How it works...Replacing missing valuesGetting readyHow to do it…How it works...Imputing missing values using machine learning modelsGetting readyHow to do it…How it works...
Chapter 10: Performing Automated Exploratory Data Analysis in Python
Technical requirementsDoing Automated EDA using pandas profilingGetting readyHow to do it…How it works...See also…Performing Automated EDA using dtaleGetting readyHow to do it…How it works...See alsoDoing Automated EDA using AutoVizGetting readyHow to do it…How it works...See alsoPerforming Automated EDA using SweetvizGetting readyHow to do it…How it works...See alsoImplementing Automated EDA using custom functionsGetting readyHow to do it…How it works...There’s more…
Index
Why subscribe?
Other Books You May EnjoyPackt is searching for authors like youShare Your ThoughtsDownload a free PDF copy of this book

Content preview from Exploratory Data Analysis with Python Cookbook

3 Visualizing Data in Python

Visualizing data is a critical component of Exploratory Data Analysis (EDA). It helps us understand relationships, patterns, and hidden trends within our data much better. With the right charts or visuals, trends within large and complex datasets can be easily interpreted, and hidden patterns or outliers can be easily identified. In Python, data can be visualized using a wide array of libraries, and in this chapter, we will explore the most common data visualization libraries in Python.

In this chapter, we will cover the following key topics:

Preparing for visualization
Visualizing data in Matplotlib
Visualizing data in Seaborn
Visualizing data in GGPLOT
Visualizing data in Bokeh

Technical requirements

We will ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Hands-On Exploratory Data Analysis with Python

Publisher Resources

ISBN: 9781803231105

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Exploratory Data Analysis with Python Cookbook

by Ayodele Oluleye

3

Visualizing Data in Python

Technical requirements

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.